def main(argv=None):
"""docstring for main"""
from enthought.mayavi import mlab
if argv is None:
argv = sys.argv
x1 = x2 = y1 = y2 = 0
fname = ''
try:
fname = argv[1]
x1, x2, y1, y2 = [int(i) for i in argv[2:]]
except:
print('Usage: {0} fname x1 x2 y1 y2'.format(argv[0]))
print(x1, x2, y1, y2)
ds = gdal.Open(fname)
band = ds.GetRasterBand(1)
STORED_VALUE = band.ReadAsArray(x1, y1, x2 - x1, y2 - y1)
ds = 0
# PDS label infos:
SCALING_FACTOR = 0.25
OFFSET = -8000
topo = (STORED_VALUE * SCALING_FACTOR) + OFFSET
mlab.surf(topo, warp_scale=1 / 115., vmin=1700)
mlab.colorbar(orientation='vertical',
title='Height [m]',
label_fmt='%4.0f')
mlab.show()
def newSurface(self):
processes = self.plotter.getProcesses()
if not self._cv_dlg:
self._cv_dlg = daeChooseVariable(daeChooseVariable.plot3D)
self._cv_dlg.updateProcessesList(processes)
self._cv_dlg.setWindowTitle('Choose variable for 3D plot')
if self._cv_dlg.exec_() != QtWidgets.QDialog.Accepted:
return False
variable, domainIndexes, domainPoints, xAxisLabel, yAxisLabel, zAxisLabel, xPoints, yPoints, zPoints, currentTime = self._cv_dlg.getPlot3DData(
)
xPoints = numpy.array(xPoints)
yPoints = numpy.array(yPoints)
xmax = numpy.max(xPoints)
ymax = numpy.max(yPoints)
zmax = numpy.max(zPoints)
xmin = numpy.min(xPoints)
ymin = numpy.min(yPoints)
zmin = numpy.min(zPoints)
warp = 'auto'
#if((xmax == xmin) or (ymax == ymin) or (zmax == zmin)):
# warp = 'auto'
#else:
# warp = math.sqrt( (xmax-xmin)*(ymax-ymin) ) / (zmax-zmin)
# colormap='gist_earth', 'RdBu'
stype = 'surface'
mlab.figure()
if (stype == 'surface'):
#print "warp=", warp
#print "[xmin, xmax, ymin, ymax, zmin, zmax]=", [xmin, xmax, ymin, ymax, zmin, zmax]
mlab.surf(xPoints,
yPoints,
zPoints,
warp_scale=warp,
representation='surface')
mlab.colorbar(orientation='vertical')
#mlab.title('polar mesh')
#mlab.outline()
mlab.axes(ranges=[xmin, xmax, ymin, ymax, zmin, zmax], nb_labels=3)
mlab.xlabel(xAxisLabel)
mlab.ylabel(yAxisLabel)
mlab.zlabel(zAxisLabel)
elif (stype == 'map'):
mlab.imshow(zPoints)
mlab.colorbar(orientation='vertical')
#mlab.title('polar mesh')
#mlab.outline()
mlab.axes(ranges=[xmin, xmax, ymin, ymax], nb_labels=3)
mlab.xlabel(xAxisLabel)
mlab.ylabel(yAxisLabel)
mlab.zlabel(zAxisLabel)
mlab.show()
def main(argv=None):
"""docstring for main"""
from enthought.mayavi import mlab
if argv is None:
argv = sys.argv
x1 = x2 = y1 = y2 = 0
fname = ""
try:
fname = argv[1]
x1, x2, y1, y2 = [int(i) for i in argv[2:]]
except:
print("Usage: {0} fname x1 x2 y1 y2".format(argv[0]))
print(x1, x2, y1, y2)
ds = gdal.Open(fname)
band = ds.GetRasterBand(1)
STORED_VALUE = band.ReadAsArray(x1, y1, x2 - x1, y2 - y1)
ds = 0
# PDS label infos:
SCALING_FACTOR = 0.25
OFFSET = -8000
topo = (STORED_VALUE * SCALING_FACTOR) + OFFSET
mlab.surf(topo, warp_scale=1 / 115.0, vmin=1700)
mlab.colorbar(orientation="vertical",
title="Height [m]",
label_fmt="%4.0f")
mlab.show()
def mlab3Dview():
e_arr = orthogonalize([x, w])
n_e_arr = [ e / np.max(np.fabs(e)) for e in e_arr ]
n_mu_q_arr = mu_q_arr / np.max(np.fabs(mu_q_arr))
m.surf(n_e_arr[0], n_e_arr[1], n_mu_q_arr)
m.show()
def add_map(colormap='Accent', opacity=1):
"Adds the land/water mask to the 3d mask as a basemap."
# The maximum x and y values are equal to the number of rows and columns,
# respectively, in the mask raster. This is done to make the map line up with
# the scalar density field.
mlab.surf(d.root.unmasked[:].astype('float'),
extent=[0,d.root.unmasked.shape[0],0,d.root.unmasked.shape[1],0,1],
colormap=colormap,
opacity=opacity)
def add_mean(color=(.9,0,.3), opacity=.3):
"Adds the mean to the map as a 3d surface."
mlab.surf(d.root.mean[:],
# The maximum x and y values are equal to the number of rows and columns,
# respectively, in the mask raster. The maximum z value is equal to the
# number of bins in the density field. This is done to make the surface line
# up with the scalar density field.
extent=[0,d.root.unmasked.shape[0],0,d.root.unmasked.shape[1],0,d.root.density_field.shape[-1]*d.root.mean[:].max()],
color=color,
# colormap='Accent',
opacity=opacity)
def test_colorbar(self):
""" Test that when an object with scalars hidden is created, it
does not get a colorbar, unless no other is avalaible.
"""
a = np.random.random((5, 5))
s1 = mlab.surf(a, colormap='gist_earth')
s2 = mlab.surf(a, color=(0, 0, 0))
mlab.colorbar()
self.assertEqual(s2.module_manager.scalar_lut_manager.show_scalar_bar,
False)
self.assertEqual(s1.module_manager.scalar_lut_manager.show_scalar_bar,
True)
def test_colorbar(self):
""" Test that when an object with scalars hidden is created, it
does not get a colorbar, unless no other is avalaible.
"""
a = np.random.random((5, 5))
s1 = mlab.surf(a, colormap='gist_earth')
s2 = mlab.surf(a, color=(0, 0, 0))
mlab.colorbar()
self.assertEqual(
s2.module_manager.scalar_lut_manager.show_scalar_bar,
False)
self.assertEqual(
s1.module_manager.scalar_lut_manager.show_scalar_bar,
True)
def plotsquare3d(us):
from enthought.mayavi.mlab import surf, show, colorbar, xlabel, ylabel, figure
for u in us:
figure()
Np = 60
points = uniformsquarepoints(Np)
x = points[:,0].reshape(Np,Np)
y = points[:,1].reshape(Np,Np)
up = np.real(u(points))
surf(x,y, up.reshape(Np,Np))
colorbar()
xlabel('x')
ylabel('y')
show()
def surface(fcm, idx0, idx1):
"""Plots a surface plot for 2D data."""
x = fcm[:, idx0]
y = fcm[:, idx1]
bins = int(numpy.sqrt(len(x)))
z, xedge, yedge = numpy.histogram2d(y, x, bins=[bins, bins],
range=[(numpy.min(y), numpy.max(y)),
(numpy.min(x), numpy.max(x))]
)
mlab.figure()
mlab.surf(xedge, yedge, z, warp_scale='auto')
mlab.xlabel(fcm.channels[idx0])
mlab.ylabel(fcm.channels[idx1])
mlab.zlabel('Density')
def plotframe(frameno,level=1, water_opacity=1.):
plotdata = ClawPlotData()
plotdata.outdir = outdir
print "Plotting solution from ",plotdata.outdir
plotdata = setplot(plotdata)
try:
frame = plotdata.getframe(frameno)
except:
print "Unable to get frame"
return
mlab.figure(1,bgcolor=(1,1,1),size=(700,600))
mlab.clf()
for grid in frame.grids:
if grid.level == level:
y = grid.c_center[1]
x = grid.c_center[0]
q = grid.q
eta = q[:,:,3]
h = q[:,:,0]
topo = eta - h
cutoff = 0.5
#cutoff2 = -500.
shift = 0.
scale = 1.
topo1 = scale*where(topo<cutoff, topo-shift, cutoff-shift)
#topo1 = scale*where(topo>cutoff2, topo1, nan)
eta1 = scale*where(eta<cutoff, eta-shift, cutoff-shift)
water1 = where(h>=1.e-3, eta1, nan)
scale = 12.
#mlab.mesh(x,y,topo1,colormap='Greens',vmin=-1.0, vmax=0.8)
#mlab.mesh(x,y,water1,colormap='Blues',vmin=-0.8, vmax=0.8)
mlab.surf(x,y,topo1,colormap='YlGn',warp_scale=scale,\
vmin=-0.3,vmax=0.3)
mlab.surf(x,y,water1,colormap='Blues',warp_scale=scale,\
vmin=-0.2,vmax=0.3, opacity=water_opacity)
# set the view: (Do V = view() to figure out the current view)
V = (29.157490879985176,\
67.560491214404507,\
79.798910042690324,\
array([ 0. , 1. , -0.07500005]))
mlab.view(*V)
t = frame.t
mlab.title('Time = %5.2f' % t,color=(0,0,0),height=0.1,size=0.5)
def surface(fcm, idx0, idx1):
"""Plots a surface plot for 2D data."""
x = fcm[:, idx0]
y = fcm[:, idx1]
bins = int(numpy.sqrt(len(x)))
z, xedge, yedge = numpy.histogram2d(y,
x,
bins=[bins, bins],
range=[(numpy.min(y), numpy.max(y)),
(numpy.min(x), numpy.max(x))])
mlab.figure()
mlab.surf(xedge, yedge, z, warp_scale='auto')
mlab.xlabel(fcm.channels[idx0])
mlab.ylabel(fcm.channels[idx1])
mlab.zlabel('Density')
def getData(self, data_D, data_H, name):
"""
plot passed in data as a surface
"""
#plotting
fig = mlab.figure(size=(512, 512))
cl.enqueue_read_buffer(lbm.queue, data_D, data_H).wait()
# retrieve mid cell points from cell node data
Nx = lbm.X.size - 1
Ny = lbm.Y.size - 1
x = np.zeros((Nx))
y = np.zeros((Ny))
for i in range(1, lbm.X.size):
x[i - 1] = (lbm.X[i] - lbm.X[i - 1]) / 2.0 + lbm.X[i - 1]
for i in range(1, lbm.Y.size):
y[i - 1] = (lbm.Y[i] - lbm.Y[i - 1]) / 2.0 + lbm.Y[i - 1]
s = mlab.surf(x, y, data_H, warp_scale='auto', colormap="jet")
mlab.axes(s)
sb = mlab.scalarbar(s, title=name)
self.s = s
self.data_D = data_D
self.data_H = data_H
def testVerticalSurface():
import pylab, numpy
pylab.clf()
no_of_phenotypes = 5
x, y = numpy.mgrid[
0:2 * no_of_phenotypes:1, 0:10:
1] #added a gap of 1 column between two phenotypes. one phenotype occupies two rows & two columns.
#remove the gap in x & y
needed_index_ls = [0, 5]
#for i in [0, 5]:
# for j in range(no_of_phenotypes):
# needed_index_ls.append(no_of_phenotypes*i+j)
#for i in range(0, no_of_phenotypes):
# needed_index_ls.append(2*i)
#needed_index_ls.append(3*i+1)
#y[3*i+1][1]=2
x = x[:, needed_index_ls]
y = y[:, needed_index_ls]
enrichment_matrix = numpy.ones(x.shape, numpy.float)
enrichment_matrix[:, :] = 10
enrichment_matrix[0, 0] = 3
from enthought.mayavi import mlab
mlab.clf()
#from palos.yh_mayavi import customBarchart
bar = customBarchart(x,
y,
enrichment_matrix,
x_scale=0.9,
y_scale=4.5,
opacity=1,
mode='cube',
color=(0, 1, 0),
scale_factor=1.0)
"""
#mlab.ylabel("KW")
#mlab.xlabel("Emma")
#mlab.zlabel("Enrichment Ratio")
from palos.DrawMatrix import get_font
font = get_font()
for i in range(len(xlabel_ls)):
label = xlabel_ls[i]
char_width, char_height = font.getsize(label) #W is the the biggest(widest)
mlab.text(2*i, 0, label, z=0, width=char_width/1500.) #min(0.0075*len(label), 0.04))
"""
s = numpy.zeros(x.shape, numpy.int)
#s[0,1]=0.5
surf = mlab.surf(x,
y,
s,
opacity=0.6,
extent=[-1, 2 * no_of_phenotypes, -1, 10, 0.0, 0.0])
mlab.show()
def gsurf():
"""Test contour_surf on regularly spaced co-ordinates like MayaVi."""
def f(x, y):
return x**2+y**2 - 25.
x, y = numpy.mgrid[-10.:10.:0.25, -10.:10.0:0.25]
s = surf(x, y, f, warp_scale = 0.)
colorbar(s,orientation='horizontal')
return s
def plotframe(frameno,level=1, water_opacity=1.):
plotdata = ClawPlotData()
plotdata.outdir = outdir
print "Plotting solution from ",plotdata.outdir
plotdata = setplot(plotdata)
try:
frame = plotdata.getframe(frameno)
except:
print "Unable to get frame"
return
mlab.figure(1,bgcolor=(1,1,1),size=(700,600))
mlab.clf()
for grid in frame.grids:
if grid.level == level:
y = grid.c_center[1]
x = grid.c_center[0]
q = grid.q
eta = q[:,:,3]
h = q[:,:,0]
topo = eta - h
cutoff = 100.
#cutoff2 = -500.
shift = 0.
scale = 1.
topo1 = scale*where(topo<cutoff, topo-shift, cutoff-shift)
#topo1 = scale*where(topo>cutoff2, topo1, nan)
eta1 = scale*where(eta<cutoff, eta-shift, cutoff-shift)
water1 = where(h>=1.e-3, eta1, nan)
scale = 10.
#mlab.mesh(x,y,topo1,colormap='Greens',vmin=-1.0, vmax=0.8)
#mlab.mesh(x,y,water1,colormap='Blues',vmin=-0.8, vmax=0.8)
if 0:
mlab.surf(x,y,topo1,colormap='YlGn',warp_scale=scale,\
vmin=-0.3,vmax=0.3)
mlab.surf(x,y,water1,colormap='Blues',warp_scale=scale,\
vmin=-0.2,vmax=0.3, opacity=water_opacity)
# set the view: (Do V = view() to figure out the current view)
#mlab.view(*V)
t = frame.t
def do(self):
############################################################
# Imports.
from enthought.mayavi import mlab
############################################################
# Create a new scene and set up the visualization.
s = self.new_scene()
############################################################
# run all the "test_foobar" functions in the mlab module.
for name, func in getmembers(mlab):
if not callable(func) or not name[:4] in ('test', 'Test'):
continue
mlab.clf()
func()
# Mayavi has become too fast: the operator cannot see if the
# Test function was succesful.
sleep(0.1)
############################################################
# Test some specific corner-cases
import numpy
x, y, z = numpy.mgrid[1:10, 1:10, 1:10]
u, v, w = numpy.mgrid[1:10, 1:10, 1:10]
s = numpy.sqrt(u**2 + v**2)
mlab.clf()
# Test the extra argument "scalars"
mlab.quiver3d(x, y, z, u, v, w, scalars=s)
# Test surf with strange-shaped inputs
X, Y = numpy.ogrid[-10:10, -10:10]
Z = X**2 + Y**2
mlab.surf(X, Y, Z)
mlab.surf(X.ravel(), Y.ravel(), Z)
x, y, z = numpy.mgrid[-10:10, -10:10, -3:2]
mlab.flow(x, y, z)
# Test glyphs with number-only coordinnates
mlab.points3d(0, 0, 0, resolution=50)
def add_quantiles(quantiles=[.25, .5, .75], colors=None, opacities = None):
"""
Adds the given quantiles to the map as 3d surfaces.
Colors and opacities can be set individually.
"""
# Default color and opacity values.
if colors is None:
colors = [(.9,0,.3)]*len(quantiles)
if opacities is None:
opacities = [.3]*len(quantiles)
# Get the empirical CDF of each pixel.
cu_data = np.cumsum(d.root.density_field[:], axis=-1)
cu_data /= cu_data.max()
for quantile,color,opacity in zip(quantiles,colors,opacities):
# Compute the actual quantile surface.
# FIXME: Use histogram_finalize for speed
s = np.zeros(d.root.unmasked.shape)
for i in xrange(cu_data.shape[0]):
for j in xrange(cu_data.shape[1]):
for k in xrange(cu_data.shape[2]):
if cu_data[i,j,k]>quantile:
s[i,j] = k
break
# Normalize
s /= cu_data.shape[2]
mlab.surf(s,
# The maximum x and y values are equal to the number of rows and columns,
# respectively, in the mask raster. The maximum z value is equal to the
# number of bins in the density field. This is done to make the surface line
# up with the scalar density field.
extent=[0,d.root.unmasked.shape[0],0,d.root.unmasked.shape[1],0,d.root.density_field.shape[-1]*s.max()],
color=color,
# colormap='Accent',
opacity=opacity)
def plot_wireframe(self, lib):
""" plot the fin wireframe """
#lib = 'mayavi'
#Filter to lower resolution
xfilter = arange(0,len(self.x_axis),len(self.x_axis)/1)
yfilter = arange(0,len(self.y_axis),len(self.y_axis)/1)
x = self.x_axis[xfilter]
y = self.y_axis[yfilter]
z = self.surf[xfilter,:]
z = z[:,yfilter]
if lib == 'gnuplot':
import Gnuplot
z[isnan(z)] = 5
g = Gnuplot.Gnuplot()
g.splot(Gnuplot.GridData(z, x, y, binary=0))
raw_input('Please press return to continue...\n')
elif lib == 'matplotlib':
import mpl_toolkits.mplot3d.axes3d as p3
fig=plt.figure(3)
ax = p3.Axes3D(fig)
x2 = zeros((100,100))
x2[:,:]=x[:]
y2 = zeros((100,100))
y2[:,:]=y[:]
ax.plot_wireframe(x2, y2.transpose(), z.transpose())
elif lib == 'mayavi':
from enthought.mayavi.mlab import surf
z[isnan(z)] = 0
surf(x, y, z)
def test_surf():
"""Test surf on regularly spaced co-ordinates like MayaVi."""
def f(x, y):
omega = numpy.sqrt(10.)
sinh, cosh = numpy.sinh, numpy.cosh
resp = numpy.zeros_like(x)
resp[x<1.55] = cosh(omega*x[x<1.55])/cosh(omega*1.55)
resp[x>=1.55] = cosh(-omega*(x[x>=1.55]-3.1))/cosh(omega*1.55)
return resp
x, y = numpy.mgrid[0.:3.1:100j, 0.:2.1:2j]
s = surf(x, y, f)
#, warp_scale = 0.05)
#cs = contour_surf(x, y, f, contour_z=0)
return s
def main():
planner = Planner()
rospy.init_node('harlie_obstacle_planner')
rospy.Subscriber("/odom", Odometry, setRobotPose,planner)
rospy.Subscriber("/move_base/global_costmap/inflated_obstacles", GridCells, setObstacles,planner)
rate = rospy.client.Rate(.1)
while not rospy.is_shutdown():
print time.time()
goal = planner.runUpdatePlanner()
if goal:
sendGoal(goal)
#Show some data
FUL=120
data=split(planner.FullCost,planner.robot.x-FUL,planner.robot.y-FUL,planner.robot.x+FUL,planner.robot.y+FUL)
l = mlab.surf(data)
#mlab.show()
rate.sleep()
def testVerticalSurface():
import pylab, numpy
pylab.clf()
no_of_phenotypes = 5
x, y = numpy.mgrid[0:2*no_of_phenotypes:1, 0:10:1] #added a gap of 1 column between two phenotypes. one phenotype occupies two rows & two columns.
#remove the gap in x & y
needed_index_ls = [0,5]
#for i in [0, 5]:
# for j in xrange(no_of_phenotypes):
# needed_index_ls.append(no_of_phenotypes*i+j)
#for i in range(0, no_of_phenotypes):
# needed_index_ls.append(2*i)
#needed_index_ls.append(3*i+1)
#y[3*i+1][1]=2
x = x[:, needed_index_ls]
y = y[:, needed_index_ls]
enrichment_matrix = numpy.ones(x.shape, numpy.float)
enrichment_matrix[:,:] =10
enrichment_matrix[0,0]=3
from enthought.mayavi import mlab
mlab.clf()
from pymodule.yh_mayavi import customBarchart
bar = customBarchart(x, y , enrichment_matrix, y_scale=4.5, opacity=1, mode='cube', color=(0,1,0), scale_factor=1.0, x_scale=0.9)
"""
#mlab.ylabel("KW")
#mlab.xlabel("Emma")
#mlab.zlabel("Enrichment Ratio")
from pymodule.DrawMatrix import get_font
font = get_font()
for i in range(len(xlabel_ls)):
label = xlabel_ls[i]
char_width, char_height = font.getsize(label) #W is the the biggest(widest)
mlab.text(2*i, 0, label, z=0, width=char_width/1500.) #min(0.0075*len(label), 0.04))
"""
s = numpy.zeros(x.shape, numpy.int)
#s[0,1]=0.5
surf = mlab.surf(x, y, s, opacity=0.6, extent=[-1, 2*no_of_phenotypes, -1, 10, 0.0,0.0])
mlab.show()
def plot3dtraj (self, behavior = 'landing', fig = 1, stim = 1):
# initialize 3d plot:
#f = mlab.figure(fig)
# plot the stimulus
if stim:
radius = self.stimulus.radius
post_height = self.stimulus.center[2]
post_bottom = -.1
step = 0.0005
x, y = np.mgrid[-1*radius-step:radius+step:step, -1*radius-step:radius+step:step]
z = x*0
for i in range(x.shape[0]):
for j in range(y.shape[1]):
if (x[i,j]**2 + y[i,j]**2) < radius**2:
z[i,j] = post_height
else:
z[i,j] = post_bottom
s = mlab.surf(x, y, z, color=(0.01,0.01,0.01) )
if 1:
# find max speed for normalizing colors
max_speed = 0
for k,v in self.trajecs.items():
if self.trajecs[k].behavior is behavior:
max_speed_tmp = self.trajecs[k].speed.max()
if max_speed_tmp > max_speed:
max_speed = max_speed_tmp
# plot trajectories of chosen behavior
for k,v in self.trajecs.items():
if self.trajecs[k].behavior is behavior:
colors = self.trajecs[k].speed / max_speed
x = self.trajecs[k].positions[:,0]
y = self.trajecs[k].positions[:,1]
z = self.trajecs[k].positions[:,2]
mlab.points3d(x, y, z, colors, colormap="jet", scale_mode='none')
print "REMEMBER TO RUN IPYTHON USING --WTHREAD"
def mcrtmv(frames, dt, Lx, Ly, Nx, Ny, savemovie=False, mvname='test'):
x = np.linspace(0, Lx, Nx)
y = np.linspace(0, Lx, Nx)
X, Y = np.meshgrid(x, y)
size = 500, 500
fig = ml.figure(size=size, bgcolor=(1., 1., 1.))
#fig.scene.anti_aliasing_frames=07
#extent = [0,Nx-1,0,Ny-1,-30,30]
ml.clf(figure=fig)
u = np.loadtxt('data/solution_%06d.txt' % 1)
fname = 'data/_tmp%07d.png' % 1
s = ml.surf(x, y, u, figure=fig, vmin=-1, vmax=1)
ml.axes(extent=[0, Lx, 0, Ly, -2, 2])
ml.colorbar()
ml.xlabel('x position')
ml.ylabel('y position')
ml.zlabel('wave amplitude')
if savemovie == True:
pl.ion()
arr = ml.screenshot()
img = pl.imshow(arr)
pl.axis('off')
for i in range(2, frames):
u = np.loadtxt('data/solution_%06d.txt' % i)
s.mlab_source.scalars = u
fname = 'data/_tmp%07d.png' % i
if savemovie == True:
arr = ml.screenshot()
img.set_array(arr)
pl.savefig(filename=fname) #,figure=fig)
print 'Saving frame', fname
pl.draw()
fig.scene.disable_render = False
os.system(
"mencoder 'mf://data/_tmp*.png' -mf type=png:fps=20 -ovc lavc -lavcopts vcodec=wmv2 -oac copy -o %s.mpg"
% mvname)
def mcrtmv(frames, dt,Lx,Ly,Nx,Ny,savemovie=False, mvname='test'):
x = np.linspace(0,Lx,Nx);
y = np.linspace(0,Lx,Nx);
X,Y = np.meshgrid(x,y);
size = 500,500
fig = ml.figure(size= size, bgcolor=(1.,1.,1.));
#fig.scene.anti_aliasing_frames=07
#extent = [0,Nx-1,0,Ny-1,-30,30]
ml.clf(figure=fig)
u = np.loadtxt('data/solution_%06d.txt'%1);
fname = 'data/_tmp%07d.png' % 1
s = ml.surf(x,y,u,figure=fig,vmin=-1,vmax=1)
ml.axes(extent=[0,Lx,0,Ly,-2,2])
ml.colorbar()
ml.xlabel('x position')
ml.ylabel('y position')
ml.zlabel('wave amplitude')
if savemovie == True:
pl.ion()
arr = ml.screenshot()
img = pl.imshow(arr)
pl.axis('off')
for i in range(2,frames):
u = np.loadtxt('data/solution_%06d.txt'%i);
s.mlab_source.scalars = u
fname = 'data/_tmp%07d.png' % i
if savemovie == True:
arr = ml.screenshot()
img.set_array(arr)
pl.savefig(filename=fname)#,figure=fig)
print 'Saving frame', fname
pl.draw()
fig.scene.disable_render = False
os.system("mencoder 'mf://data/_tmp*.png' -mf type=png:fps=20 -ovc lavc -lavcopts vcodec=wmv2 -oac copy -o %s.mpg" % mvname);
def main():
planner = Planner()
rospy.init_node('harlie_obstacle_planner')
rospy.Subscriber("/odom", Odometry, setRobotPose, planner)
rospy.Subscriber("/move_base/global_costmap/inflated_obstacles", GridCells,
setObstacles, planner)
rate = rospy.client.Rate(.1)
while not rospy.is_shutdown():
print time.time()
goal = planner.runUpdatePlanner()
if goal:
sendGoal(goal)
#Show some data
FUL = 120
data = split(planner.FullCost, planner.robot.x - FUL,
planner.robot.y - FUL, planner.robot.x + FUL,
planner.robot.y + FUL)
l = mlab.surf(data)
#mlab.show()
rate.sleep()
def plot(self):
"绘制场景"
# ball.up.x = 5
# ball.up.y = 6
# ball.up.z = 7
# ball.up.x = -4
# ball.up.y = -3
# ball.up.z = -2
x = [[-1,1,1,-1,-1],
[-1,1,1,-1,-1]]
y = [[-1,-1,-1,-1,-1],
[1,1,1,1, 1]]
z = [[1,1,-1,-1,1],
[1,1,-1,-1,1]]
# s = mlab.mesh(x, y, z, representation="wireframe", line_width=1.0 )
# mlab.show()
pl = mlab.surf(x,y,z,warp_scale="auto")
mlab.axes(xlabel = 'x',ylabel= 'y',zlabel= 'z')
mlab.outline(pl)
def show(self):
"""show Vm in a graph. Works for 1D projects only"""
if self.Vm.ndim == 2:
pylab.imshow(self.Vm,aspect='auto',cmap=cm.jet)
pylab.show()
elif self.Vm.ndim == 3:
s = mlab.surf(self.Vm[...,0])
raw_input("Press Enter to lauch the simulation...")
for i in range(self.Vm.shape[-1]):
s.mlab_source.scalars = self.Vm[...,i]
elif self.Vm.ndim == 4:
p = mlab.pipeline.scalar_field(self.Vm[...,0])
s = mlab.pipeline.image_plane_widget( p,
plane_orientation='x_axes',
slice_index=self.mdl.stimCoord[0],
vmin = self.Vm.min(),
vmax = self.Vm.max()
)
s2 = mlab.pipeline.image_plane_widget(p,
plane_orientation='y_axes',
slice_index=self.mdl.stimCoord[2],
vmin = self.Vm.min(),
vmax = self.Vm.max()
)
s3 = mlab.pipeline.image_plane_widget( p,
plane_orientation='z_axes',
slice_index=self.mdl.stimCoord[4],
vmin = self.Vm.min(),
vmax = self.Vm.max()
)
mlab.scalarbar(s,orientation='vertical',nb_labels=4,label_fmt='%.3f')
mlab.outline(color=(1,1,1))
raw_input("Press Enter to lauch the simulation...")
for i in range(self.Vm.shape[-1]):
p.mlab_source.scalars = self.Vm[...,i]
# -*- coding: utf-8 -*-
from enthought.mayavi import mlab
import numpy as np
x, y = np.ogrid[-10:10:100j, -1:1:100j]
z = np.sin(5 * ((x / 10)**2 + y**2))
mlab.figure(bgcolor=(1, 1, 1), fgcolor=(0, 0, 0))
mlab.surf(x, y, z)
mlab.axes()
mlab.figure(bgcolor=(1, 1, 1), fgcolor=(0, 0, 0))
mlab.surf(x, y, z, extent=(-1, 1, -1, 1, -0.5, 0.5))
mlab.axes(nb_labels=5)
mlab.figure(bgcolor=(1, 1, 1), fgcolor=(0, 0, 0))
mlab.surf(x, y, z, extent=(-1, 1, -1, 1, -0.5, 0.5))
mlab.axes(ranges=(x.min(), x.max(), y.min(), y.max(), z.min(), z.max()),
nb_labels=5)
mlab.show()
def show(self):
"""Test contour_surf on regularly spaced co-ordinates like MayaVi."""
x, y = self.out_grid
s = surf(x, y, self.scalars, warp_scale = 0.5)
colorbar(s)
return s
#!//usr/bin/env python
import math
from enthought.mayavi import mlab
Zs, Xs, Ys = [], [], []
for p in range(360):
for t in range(180):
phi = p * math.pi / 180.
theta = t * math.pi / 180.
Xs.append(math.sin(phi) * math.cos(theta))
Ys.append(math.cos(phi))
Zs.append(math.sin(phi) * math.sin(theta))
s = mlab.surf(Xs, Ys, Zs)
mlab.show()
raw_input()
colormap, but we modify add to add a transparency effect. Notice in the resulting image how the surface becomes more transparent for its lower points. """ # Create some data import numpy as np x, y = np.mgrid[-10:10:200j, -10:10:200j] z = 100 * np.sin(x * y) / (x * y) # Visualize it with mlab.surf from enthought.mayavi import mlab mlab.figure(bgcolor=(1, 1, 1)) surf = mlab.surf(z, colormap='cool') # Retrieve the LUT of the surf object. lut = surf.module_manager.scalar_lut_manager.lut.table.to_array() # The lut is a 255x4 array, with the columns representing RGBA # (red, green, blue, alpha) coded with integers going from 0 to 255. # We modify the alpha channel to add a transparency gradient lut[:, -1] = np.linspace(0, 255, 256) # and finally we put this LUT back in the surface object. We could have # added any 255*4 array rather than modifying an existing LUT. surf.module_manager.scalar_lut_manager.lut.table = lut # We need to force update of the figure now that we have changed the LUT. mlab.draw()
# 菱形平均分两步，分别计算水平和垂直方向上的点
t = a[::s2, s::s2]
t[...] = buf[:, :-1] + buf[:, 1:]
t[:-1] += tmp
t[1:] += tmp
t[[0, -1], :] /= 3 # 边上是3个值的平均
t[1:-1, :] /= 4 # 中间的是4个值的平均
t[...] += np.random.normal(0, scale, t.shape)
t = a[s::s2, ::s2]
t[...] = buf[:-1, :] + buf[1:, :]
t[:, :-1] += tmp
t[:, 1:] += tmp
t[:, [0, -1]] /= 3
t[:, 1:-1] /= 4
t[...] += np.random.normal(0, scale, t.shape)
scale *= d
return a
if __name__ == "__main__":
from enthought.mayavi import mlab
from scipy.ndimage.filters import convolve
a = hill2d_ds(8, 0.5)
a /= np.ptp(a) / (0.5 * 2**8)
a = convolve(a, np.ones((3, 3)) / 9.0)
mlab.surf(a)
mlab.show()
# -*- coding: utf-8 -*- """ Created on Wed Jul 20 22:27:33 2016 @author: liuxiangyu """ import numpy as np from enthought.mayavi import mlab x, y = np.ogrid[-2:2:20j, -2:2:20j] z = x * np.exp(-x**2 - y**2) pl = mlab.surf(x, y, z, warp_scale="auto") mlab.axes(xlabel='x', ylabel='y', zlabel='z') mlab.outline(pl) mlab.show()
#!/usr/bin/env python import numpy as np from enthought.mayavi import mlab x, y = np.ogrid[-2:2:160j, -2:2:160j] z = abs(x) * np.exp(-x**2 - (y / .75)**2) pl = mlab.surf(x, y, z, warp_scale=2) mlab.axes(xlabel='x', ylabel='y', zlabel='z') mlab.outline(pl) mlab.show()
# -*- coding: utf-8 -*-
"""
Created on Sun Oct 17 22:10:35 2010
@author: -
"""
import numpy
from enthought.mayavi import mlab
x, y = numpy.mgrid[0:3:1, 0:3:1]
s = mlab.surf(x, y, numpy.asarray(x * 0.1, 'd'))
#
for i in range(10):
s.mlab_source.scalars = numpy.asarray(x * 0.1 * (i + 1), 'd')
#
# Produce some nice data.
n_mer, n_long = 6, 11
pi = numpy.pi
dphi = pi / 1000.0
phi = numpy.arange(0.0, 2 * pi + 0.5 * dphi, dphi, 'd')
mu = phi * n_mer
x = numpy.cos(mu) * (1 + numpy.cos(n_long * mu / n_mer) * 0.5)
y = numpy.sin(mu) * (1 + numpy.cos(n_long * mu / n_mer) * 0.5)
z = numpy.sin(n_long * mu / n_mer) * 0.5
# View it.
l = mlab.plot3d(x, y, z, numpy.sin(mu), tube_radius=0.025, colormap='Spectral')
# Now animate the data.
ms = l.mlab_source
for i in range(10):
import numpy as np from enthought.mayavi import mlab x, y = np.ogrid[-2:2:20j, -2:2:20j] z = x * np.exp(-x**2 - y**2) pl = mlab.surf(x, y, z, warp_scale='auto') mlab.axes(xlabel='x', ylabel='y', zlabel='z') mlab.outline(pl)
import sys
from enthought.mayavi import mlab
import numpy as np
for i in sys.argv:
print i
if (len(sys.argv)>3):
# read xsm data
filename=sys.argv[3]
f=open(filename)
header = f.readline()
nx,ny = header.split()[2].split('x')
nx = int(nx)
ny = int(ny)
print "nx : "+str(nx)
print "ny : "+str(ny)
read_data = np.fromfile(file=f, dtype=np.float32).reshape((nx,ny))
f.close()
# 3D visualization of data:
#[x,y]=np.mgrid[0:1.0:1.0/nx,0:1.0:1.0/ny]
mlab.figure(size=(400, 300))
mlab.surf(read_data, warp_scale='auto')
mlab.show()
else:
print "You must provide data filename"
print "usage: mayavi2 -x heat.py filename"
bgcolor=(0.5, 0.5, 0.5))
mlab.clf()
cat1 = cat(x, y, 1)
cat2 = cat(x, y, 2)
cat3 = cat(x, y, 3)
# The cats lie in a [0, 1] interval, with .5 being the assymptotique
# value. We want to reposition this value to 0, so as to put it in the
# center of our extents.
cat1 -= 0.5
cat2 -= 0.5
cat3 -= 0.5
cat1_extent = (-14,-6, -4,4, 0,5)
surf_cat1 = mlab.surf(x-10, y, cat1, colormap='Spectral', warp_scale=5,
extent=cat1_extent, vmin=-0.5, vmax=0.5)
mlab.outline(surf_cat1, color=(.7, .7, .7))
mlab.axes(surf_cat1, color=(.7, .7, .7), extent=cat1_extent,
ranges=(0,1, 0,1, 0,1), xlabel='', ylabel='',
zlabel='Probability',
x_axis_visibility=False, z_axis_visibility=False)
mlab.text(-18, -4, '1 photon', z=-4, width=0.13)
cat2_extent = (-4,4, -4,4, 0,5)
surf_cat2 = mlab.surf(x, y, cat2, colormap='Spectral', warp_scale=5,
extent=cat2_extent, vmin=-0.5, vmax=0.5)
mlab.outline(surf_cat2, color=(0.7, .7, .7), extent=cat2_extent)
mlab.text(-4, -3, '2 photons', z=-4, width=0.14)
import pylab as py
def func(x, y):
return (x + y) * np.exp(-5.0 * (x**2 + y**2))
x, y = np.mgrid[-1:1:15j, -1:1:15j]
fvals = func(x, y)
newfunc = interpolate.interp2d(x, y, fvals, kind='cubic')
# Notice that we evaluate on new 1-d arrays
xnew = np.linspace(-1, 1, 100)
ynew = xnew
# Return the cross product
fnew = newfunc(xnew, ynew)
py.figure(1)
py.clf()
py.imshow(fnew, extent=[-1, 1, -1, 1], cmap=py.cm.jet)
from enthought.mayavi import mlab
# Need fully-fleshed out grid for surf
x2, y2 = np.mgrid[-1:1:100j, -1:1:100j]
mlab.clf()
mlab.surf(x2, y2, fnew * 2)
opener = urllib.urlopen(
'http://staging.enthought.com/projects/mayavi/N36W113.hgt.zip'
#'ftp://e0srp01u.ecs.nasa.gov/srtm/version2/SRTM1/Region_04/N36W113.hgt.zip'
)
open('N36W113.hgt.zip', 'w').write(opener.read())
# Load the data (signed 2 byte integers, big endian) ###########################
import zipfile
import numpy as np
data = np.fromstring(
zipfile.ZipFile('N36W113.hgt.zip').read('N36W113.hgt'), '>i2')
data.shape = (3601, 3601)
data = data.astype(np.float32)
# Plot an interesting section ##################################################
from enthought.mayavi import mlab
data = data[:1000, 900:1900]
# Convert missing values into something more sensible.
data[data == -32768] = data[data > 0].min()
mlab.figure(size=(400, 320), bgcolor=(0.16, 0.28, 0.46))
mlab.surf(data, colormap='gist_earth', warp_scale=0.2, vmin=1200, vmax=1610)
# The data takes a lot of memory, and the surf command has created a
# copy. We free the inital memory.
del data
# A view of the canyon
mlab.view(-5.9, 83, 570, [5.3, 20, 238])
mlab.show()
import numpy as np x, y = np.mgrid[-10:10:100j, -10:10:100j] r = np.sqrt(x**2 + y**2) z = np.sin(r) / r from enthought.mayavi import mlab mlab.surf(z, warp_scale='auto') mlab.outline() mlab.axes()
def pattern_tp(array_ip, tht_scan=0, phi_scan=0, tht_min=0, tht_max=np.pi, tht_num=50,
phi_min=0, phi_max=2 * np.pi, phi_num=50, scale="dB",
dB_limit= -40, factor="GF", plot_type="rect",
mayavi_app=False):
r"""
Function to evaluate 3D AF/GF/NF of a arbitrary 3D array in (tht, phi)-domain.
By default, this function calculates the gain-factor (GF).
:param array_ip: array excitation data in 'Arraytool' input format (see :func:`ip_format`)
:param tht_scan, etc: beam scan position in (tht, phi)-domain
:param tht_min, etc: limits of (tht, phi)-domain
:param tht_num, etc: number of points between 'tht_min' and 'tht_max' including
the boundaries
:param scale: specifies the scale choice ... dB/linear
:param dB_limit: cutoff limit (see :func:`cutoff`)
:param factor: type of pattern you need ... AF/NF/GF
:param plot_type: can be rect/polar/contour ... if False, nothing happens
:param mayavi_app: if True, the 3D plot will be opened in the MayaVi application
:rtype: A list, [tht,phi,F]
"""
x = array_ip[:, 0]
y = array_ip[:, 1]
z = array_ip[:, 2]
A = array_ip[:, 3] # un-packing "array_ip" finished
k = 2 * np.pi # (angular) wave-number, which is 2*pi when lambda = 1
tht_numj = complex(0, tht_num)
phi_numj = complex(0, phi_num)
[tht, phi] = np.mgrid[tht_min:tht_max:tht_numj, phi_min:phi_max:phi_numj]
u = np.sin(tht) * np.cos(phi); v = np.sin(tht) * np.sin(phi); w = np.cos(tht)
u1 = np.reshape(u, (u.size, -1))
v1 = np.reshape(v, (v.size, -1))
w1 = np.reshape(w, (w.size, -1))
u_scan = np.sin(tht_scan) * np.cos(phi_scan)
v_scan = np.sin(tht_scan) * np.sin(phi_scan)
w_scan = np.cos(tht_scan)
A = np.reshape(A, (len(A), -1))
U = np.tile(u1 - u_scan, len(x))
V = np.tile(v1 - v_scan, len(x))
W = np.tile(w1 - w_scan, len(x))
X = np.tile(x, (u.size, 1))
Y = np.tile(y, (u.size, 1))
Z = np.tile(z, (u.size, 1))
# Evaluating array-factor of the planar array
AF1 = np.dot(np.exp(1j * k * (U * X + V * Y + W * Z)), A)
AF = np.reshape(AF1, u.shape)
# Evaluation of F = (AF/GF/NF) => depending upon the user's choice
if(factor == "AF"):
F = AF; n1 = ""; ff = "Array-Factor "; f1 = "AF "
elif(factor == "GF"):
P_inc = ((abs(A)) ** 2).sum()
GF = AF / np.sqrt(P_inc) # Converting the AF to GF
F = GF; n1 = ""; ff = "Gain-Factor "; f1 = "GF "
elif(factor == "NF"):
norm_fact = (abs(A)).sum()
F = AF / norm_fact
n1 = "Normalized "; ff = "Factor "; f1 = "NF "
# converting 'F' from linear to dB scale, if needed
if(scale == "linear"):
F_plt = abs(F)
ss = "in linear scale"
elif(scale == "dB"):
F = 20 * np.log10(abs(F))
# cutoff the "F" below some limit ... just for the plotting purpose
F_plt = cutoff(F, dB_limit)
ss = "in dB scale"
# plotting the factor (AF/GF/NF)
if(plot_type):
if (mayavi_app): # opens the 3D plot in MayaVi Application
mlab.options.backend = 'envisage'
if(plot_type == "rect"): # rectangular plot
plt3d = mlab.surf(tht, phi, F_plt, warp_scale='auto')
ranges1 = [tht.min(), tht.max(), phi.min(), phi.max(), F_plt.min(), F_plt.max()]
mlab.axes(xlabel='Tht', ylabel='Phi', zlabel=f1,
ranges=ranges1, nb_labels=5)
mlab.title(n1 + ff + ss, size=0.35)
mlab.colorbar(orientation="vertical", nb_labels=5)
plt3d.scene.isometric_view()
mlab.show()
if(plot_type == "polar"): # rectangular plot
if(scale == "dB"):
F_plt = F_plt - dB_limit
F_plt_x = F_plt * u; F_plt_y = F_plt * v; F_plt_z = F_plt * w
ranges1 = [F_plt_x.min(), F_plt_x.max(), F_plt_y.min(), F_plt_y.max(), F_plt_z.min(), F_plt_z.max()]
plt3d = mlab.mesh(F_plt_x, F_plt_y, F_plt_z, scalars=F_plt, extent=ranges1)
mlab.axes(xlabel='x', ylabel='y', zlabel='z',
ranges=ranges1, nb_labels=5)
mlab.title(n1 + ff + ss, size=0.35)
mlab.colorbar(orientation="vertical", nb_labels=5)
plt3d.scene.isometric_view()
mlab.show()
if(plot_type == "contour"): # contour plot
plt.contourf(tht, phi, F_plt)
plt.axis('tight'); plt.grid(True)
plt.xlabel(r'$\theta$', fontsize=16)
plt.ylabel(r'$\phi$', fontsize=16)
plt.colorbar(format='$%.2f$')
plt.show()
return tht, phi, F
ns = data.shape[1]
ne = data.shape[2]
nz = data.shape[3]
f = mlab.figure(size=(600, 600))
# Tell visual to use this as the viewer.
visual.set_viewer(f)
# First way
s1 = contour_surf(data[0, :, :, 10] + 0.1,
contours=30,
line_width=0.5,
transparent=True)
s = surf(data[0, :, :, 10] + 0.1, colormap="Spectral"
) # , warp_scale='.1')#, representation='wireframe')
# second way
# x, y= mgrid[0:ns:1, 0:ne:1]
# s = mesh(x,y,data[0,:,:,10], colormap='Spectral')#, warp_scale='auto')
# #, representation='wireframe')
s.enable_contours = True
s.contour.filled_contours = True
#
# x, y, z= mgrid[0:ns:1, 0:ne:1, 0:nz:1]
#
# p=plot3d(x,y,z,data[10,:,:,:], tube_radius=0.025, colormap='Spectral')
# p=points3d(x,y,z,data[10,:,:,:], colormap='Spectral')
#
def pattern_tp(
array_ip,
tht_scan=0,
phi_scan=0,
tht_min=0,
tht_max=np.pi,
tht_num=50,
phi_min=0,
phi_max=2 * np.pi,
phi_num=50,
scale="dB",
dB_limit=-40,
factor="GF",
plot_type="rect",
mayavi_app=False,
):
r"""
Function to evaluate 3D AF/GF/NF of a arbitrary 3D array in (tht, phi)-domain.
By default, this function calculates the gain-factor (GF).
:param array_ip: array excitation data in 'Arraytool' input format (see :func:`ip_format`)
:param tht_scan, etc: beam scan position in (tht, phi)-domain
:param tht_min, etc: limits of (tht, phi)-domain
:param tht_num, etc: number of points between 'tht_min' and 'tht_max' including
the boundaries
:param scale: specifies the scale choice ... dB/linear
:param dB_limit: cutoff limit (see :func:`cutoff`)
:param factor: type of pattern you need ... AF/NF/GF
:param plot_type: can be rect/polar/contour ... if False, nothing happens
:param mayavi_app: if True, the 3D plot will be opened in the MayaVi application
:rtype: A list, [tht,phi,F]
"""
x = array_ip[:, 0]
y = array_ip[:, 1]
z = array_ip[:, 2]
A = array_ip[:, 3] # un-packing "array_ip" finished
k = 2 * np.pi # (angular) wave-number, which is 2*pi when lambda = 1
tht_numj = complex(0, tht_num)
phi_numj = complex(0, phi_num)
[tht, phi] = np.mgrid[tht_min:tht_max:tht_numj, phi_min:phi_max:phi_numj]
u = np.sin(tht) * np.cos(phi)
v = np.sin(tht) * np.sin(phi)
w = np.cos(tht)
u1 = np.reshape(u, (u.size, -1))
v1 = np.reshape(v, (v.size, -1))
w1 = np.reshape(w, (w.size, -1))
u_scan = np.sin(tht_scan) * np.cos(phi_scan)
v_scan = np.sin(tht_scan) * np.sin(phi_scan)
w_scan = np.cos(tht_scan)
A = np.reshape(A, (len(A), -1))
U = np.tile(u1 - u_scan, len(x))
V = np.tile(v1 - v_scan, len(x))
W = np.tile(w1 - w_scan, len(x))
X = np.tile(x, (u.size, 1))
Y = np.tile(y, (u.size, 1))
Z = np.tile(z, (u.size, 1))
# Evaluating array-factor of the planar array
AF1 = np.dot(np.exp(1j * k * (U * X + V * Y + W * Z)), A)
AF = np.reshape(AF1, u.shape)
# Evaluation of F = (AF/GF/NF) => depending upon the user's choice
if factor == "AF":
F = AF
n1 = ""
ff = "Array-Factor "
f1 = "AF "
elif factor == "GF":
P_inc = ((abs(A)) ** 2).sum()
GF = AF / np.sqrt(P_inc) # Converting the AF to GF
F = GF
n1 = ""
ff = "Gain-Factor "
f1 = "GF "
elif factor == "NF":
norm_fact = (abs(A)).sum()
F = AF / norm_fact
n1 = "Normalized "
ff = "Factor "
f1 = "NF "
# converting 'F' from linear to dB scale, if needed
if scale == "linear":
F_plt = abs(F)
ss = "in linear scale"
elif scale == "dB":
F = 20 * np.log10(abs(F))
# cutoff the "F" below some limit ... just for the plotting purpose
F_plt = cutoff(F, dB_limit)
ss = "in dB scale"
# plotting the factor (AF/GF/NF)
if plot_type:
if mayavi_app: # opens the 3D plot in MayaVi Application
mlab.options.backend = "envisage"
if plot_type == "rect": # rectangular plot
plt3d = mlab.surf(tht, phi, F_plt, warp_scale="auto")
ranges1 = [tht.min(), tht.max(), phi.min(), phi.max(), F_plt.min(), F_plt.max()]
mlab.axes(xlabel="Tht", ylabel="Phi", zlabel=f1, ranges=ranges1, nb_labels=5)
mlab.title(n1 + ff + ss, size=0.35)
mlab.colorbar(orientation="vertical", nb_labels=5)
plt3d.scene.isometric_view()
mlab.show()
if plot_type == "polar": # rectangular plot
if scale == "dB":
F_plt = F_plt - dB_limit
F_plt_x = F_plt * u
F_plt_y = F_plt * v
F_plt_z = F_plt * w
ranges1 = [F_plt_x.min(), F_plt_x.max(), F_plt_y.min(), F_plt_y.max(), F_plt_z.min(), F_plt_z.max()]
plt3d = mlab.mesh(F_plt_x, F_plt_y, F_plt_z, scalars=F_plt, extent=ranges1)
mlab.axes(xlabel="x", ylabel="y", zlabel="z", ranges=ranges1, nb_labels=5)
mlab.title(n1 + ff + ss, size=0.35)
mlab.colorbar(orientation="vertical", nb_labels=5)
plt3d.scene.isometric_view()
mlab.show()
if plot_type == "contour": # contour plot
plt.contourf(tht, phi, F_plt)
plt.axis("tight")
plt.grid(True)
plt.xlabel(r"$\theta$", fontsize=16)
plt.ylabel(r"$\phi$", fontsize=16)
plt.colorbar(format="$%.2f$")
plt.show()
return tht, phi, F
pl.gca().add_patch(rect)
pl.gca().set_aspect("equal")
pl.show()
####### 误差曲面 #######
scale_k = 1.0
scale_b = 10.0
scale_error = 1000.0
def S(k, b):
"计算直线y=k*x+b和原始数据X、Y的误差的平方和"
error = np.zeros(k.shape)
for x, y in zip(X, Y):
error += (y - (k*x + b))**2
return error
ks, bs = np.mgrid[k-scale_k:k+scale_k:40j, b-scale_b:b+scale_b:40j]
error = S(ks, bs)/scale_error
from enthought.mayavi import mlab
surf = mlab.surf(ks, bs/scale_b, error)
mlab.axes(xlabel="k", ylabel="b", zlabel="error",
ranges=[k-scale_k,k+scale_k,
b-scale_b,b+scale_b,
np.min(error)*scale_error, np.max(error)*scale_error])
mlab.outline(surf)
mlab.points3d([k],[b/scale_b],[S(k,b)/scale_error],scale_factor=0.1, color=(1,1,1))
mlab.show()
def test_solve_minimal_surface_optimization_problem_with_projected_gradients():
"""
This is a minimal surface problem, discretized on a regular mesh with box constraints using projected gradients.
The necessary gradient is computed with adolc.
"""
def projected_gradients(x0, ffcn,dffcn, box_constraints, beta = 0.5, delta = 10**-3, epsilon = 10**-2, max_iter = 1000, line_search_max_iter = 100):
"""
INPUT: box_constraints [L,U], where L (resp. U) vector or matrix with the lower (resp. upper) bounds
"""
x = x0.copy()
L = numpy.array(box_constraints[0])
U = numpy.array(box_constraints[1])
def pgn(s):
a = 1.* (x>L)
b = 1.*(abs(x-L) <0.00001)
c = 1.*(s>0)
d = numpy.where( a + (b*c))
return numpy.sum(s[d])
def P(x, s, alpha):
x_alpha = x + alpha * s
a = x_alpha-L
b = U - x_alpha
return x_alpha - 1.*(a<0) * a + b * 1. * (b<0)
s = - dffcn(x)
k = 0
while pgn(s)>epsilon and k<= max_iter:
k +=1
s = - dffcn(x)
for m in range(line_search_max_iter):
#print 'm=',m
alpha = beta**m
x_alpha = P(x,s,alpha)
if ffcn( x_alpha ) - ffcn(x) <= - delta * numpy.sum(s* (x_alpha - x)):
break
x_old = x.copy()
x = x_alpha
return x_old,s
def O_tilde(u):
""" this is the objective function"""
M = numpy.shape(u)[0]
h = 1./(M-1)
return M**2*h**2 + numpy.sum(0.25*( (u[1:,1:] - u[0:-1,0:-1])**2 + (u[1:,0:-1] - u[0:-1, 1:])**2))
# INITIAL VALUES
M = 20
h = 1./M
u = numpy.zeros((M,M),dtype=float)
u[0,:]= [numpy.sin(numpy.pi*j*h/2.) for j in range(M)]
u[-1,:] = [ numpy.exp(numpy.pi/2) * numpy.sin(numpy.pi * j * h / 2.) for j in range(M)]
u[:,0]= 0
u[:,-1]= [ numpy.exp(i*h*numpy.pi/2.) for i in range(M)]
# tape the function evaluation
trace_on(1)
au = adouble(u)
independent(au)
ay = O_tilde(au)
dependent(ay)
trace_off()
def dO_tilde(u):
ru = numpy.ravel(u)
rg = gradient(1,ru)
g = numpy.reshape(rg, numpy.shape(u))
# on the edge the analytical solution is fixed to zero
g[:,0] = 0
g[0,:] = 0
g[:,-1] = 0
g[-1,:] = 0
return g
# X AND Y PARTITION
x_grid = numpy.linspace(0,1,M)
y_grid = numpy.linspace(0,1,M)
# BOX CONSTRAINTS
lo = 2.5
L = numpy.zeros((M,M),dtype=float)
for n in range(M):
for m in range(M):
L[n,m] = 2.5 * ( (x_grid[n]-0.5)**2 + (y_grid[m]-0.5)**2 <= 1./16)
U = 100*numpy.ones((M,M),dtype=float)
Z,s = projected_gradients(u,O_tilde,dO_tilde,[L,U])
# THIS CODE BELOW ONLY WORKS FOR MATPLOTLIB 0.91X
try:
import pylab
import matplotlib.axes3d as p3
x = y = range(numpy.shape(Z)[0])
X,Y = numpy.meshgrid(x_grid,y_grid)
fig=pylab.figure()
ax = p3.Axes3D(fig)
ax.plot_wireframe(X,Y,Z)
xs = Z + s
for n in range(M):
for m in range(M):
ax.plot3d([x_grid[m],x_grid[m]], [ y_grid[n], y_grid[n]], [Z[n,m], xs[n,m]], 'r')
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
pylab.title('Minimal Surface')
pylab.savefig('./3D_plot.png')
#pylab.show()
except:
pass
# Plot with MAYAVI
try:
import enthought.mayavi.mlab as mlab
mlab.figure()
mlab.view(azimuth=130)
s = mlab.surf(x, y, Z, representation='wireframe', warp_scale='auto', line_width=1.)
mlab.savefig('./mayavi_3D_plot.png')
#mlab.show()
except:
pass
# Reshape long 1D results to 2D grid:
uu=np.zeros((n+1,n+1))
uu[1:n,1:n] = u.reshape(n-1,n-1).transpose()
xx,yy = np.meshgrid(x,y)
value = uu[n/2,n/2]
# Interpolate to finer grid and plot
uu = uu.flatten(1)
uuu = interp2d(x,y,uu,kind='cubic')
a = np.linspace(-1.,1.,100)
b = np.linspace(-1.,1.,100)
xxx,yyy = np.meshgrid(a,b)
newfunc = uuu(a,b)
# Plot results
# wireframe:
fig=p.figure()
ax = p3.Axes3D(fig)
ax.plot_wireframe(xxx,yyy,newfunc)
ax.text(-0.8,0.5,.022,'u(0,0) = %13.11f' % value)
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
p.show()
# mlab plotting
from enthought.mayavi import mlab
mlab.surf(a,b,newfunc)
mlab.show()
def pattern_uv(array_ip, u_scan=0, v_scan=0, u_min= -1, u_max=1, u_num=50,
v_min= -1, v_max=1, v_num=50, scale="dB",
dB_limit= -40, factor="GF", plot_type="rect",
mayavi_app=False):
r"""
Function to evaluate 3D AF/GF/NF of a planar array in uv-domain.
By default, this function calculates the gain-factor (GF).
:param array_ip: array excitation data in 'Arraytool' input format (see :func:`ip_format`)
:param u_scan, v_scan: beam scan position in uv-domain
:param u_min, etc: limits of uv-domain
:param u_num, v_num: number of points between 'u_min' and 'u_max' including boundaries
:param scale: specifies the scale choice ... dB/linear
:param dB_limit: cutoff limit (see :func:`cutoff`)
:param factor: type of pattern you need ... AF/NF/GF
:param plot_type: can be rect/polar ... if False, nothing happens
:param mayavi_app: if True, the 3D plot will be opened in the MayaVi application
:rtype: A list, [u,v,F]
"""
x = array_ip[:, 0]
y = array_ip[:, 1]
z = array_ip[:, 2]
A = array_ip[:, 3] # un-packing "array_ip" finished
k = 2 * np.pi # (angular) wave-number, which is 2*pi when lambda = 1
u_numj = complex(0, u_num)
v_numj = complex(0, v_num)
# Making sure all elements in the z-column of the "array_ip" are zeros
z_flag = True
if ((abs(z) > 0).sum()):
print "All elements in the z-column of array input should be zero."
z_flag = False
# After making sure, proceed to the next level, i.e., evaluate the pattern
if(z_flag):
[u, v] = np.mgrid[u_min:u_max:u_numj, v_min:v_max:v_numj]
u1 = np.reshape(u, (u.size, -1))
v1 = np.reshape(v, (v.size, -1))
A = np.reshape(A, (len(A), -1))
U = np.tile(u1 - u_scan, len(x))
V = np.tile(v1 - v_scan, len(x))
X = np.tile(x, (u.size, 1))
Y = np.tile(y, (u.size, 1))
# Evaluating array-factor of the planar array
AF1 = np.dot(np.exp(1j * k * (U * X + V * Y)), A)
AF = np.reshape(AF1, u.shape)
# Evaluation of F = (AF/GF/NF) => depending upon the user's choice
if(factor == "AF"):
F = AF; n1 = ""; ff = "Array-Factor "; f1 = "AF "
elif(factor == "GF"):
P_inc = ((abs(A)) ** 2).sum()
GF = AF / np.sqrt(P_inc) # Converting the AF to GF
F = GF; n1 = ""; ff = "Gain-Factor "; f1 = "GF "
elif(factor == "NF"):
norm_fact = (abs(A)).sum()
F = AF / norm_fact
n1 = "Normalized "; ff = "Factor "; f1 = "NF "
# converting 'F' from linear to dB scale, if needed
if(scale == "linear"):
F_plt = abs(F)
ss = "in linear scale"
elif(scale == "dB"):
F = 20 * np.log10(abs(F))
# cutoff the "F" below some limit ... just for the plotting purpose
F_plt = cutoff(F, dB_limit)
ss = "in dB scale"
# plotting the factor (AF/GF/NF)
if(plot_type):
if(plot_type == "rect"): # rectangular plot
if (mayavi_app): # opens the 3D plot in MayaVi Application
mlab.options.backend = 'envisage'
plt3d = mlab.surf(u, v, F_plt, warp_scale='auto')
ranges1 = [u_min, u_max, v_min, v_max, F_plt.min(), F_plt.max()]
mlab.axes(xlabel='u', ylabel='v', zlabel=f1,
ranges=ranges1, nb_labels=5)
mlab.title(n1 + ff + ss, size=0.35)
mlab.colorbar(orientation="vertical", nb_labels=5)
plt3d.scene.isometric_view()
mlab.show()
if(plot_type == "contour"): # contour plot
plt.contourf(u, v, F_plt)
vs = plt.Circle((0, 0), radius=1, edgecolor='w', fill=False)
ax = plt.gca(); ax.add_patch(vs)
plt.axis('image'); plt.grid(True)
plt.xlabel(r'$u,\ \mathrm{where}\ u=\sin \theta \cos \phi\ \mathrm{in}\ \mathrm{the}\ \mathrm{visible-space}$', fontsize=16)
plt.ylabel(r'$v,\ \mathrm{where}\ v=\sin \theta \sin \phi\ \mathrm{in}\ \mathrm{the}\ \mathrm{visible-space}$', fontsize=16)
plt.colorbar(format='$%.2f$')
plt.show()
return u, v, F
scale_error = 1000.0
def S(k, b):
"计算直线y=k*x+b和原始数据X、Y的误差的平方和"
error = np.zeros(k.shape)
for x, y in zip(X, Y):
error += (y - (k * x + b))**2
return error
ks, bs = np.mgrid[k - scale_k:k + scale_k:40j, b - scale_b:b + scale_b:40j]
error = S(ks, bs) / scale_error
from enthought.mayavi import mlab
surf = mlab.surf(ks, bs / scale_b, error)
mlab.axes(xlabel="k",
ylabel="b",
zlabel="error",
ranges=[
k - scale_k, k + scale_k, b - scale_b, b + scale_b,
np.min(error) * scale_error,
np.max(error) * scale_error
])
mlab.outline(surf)
mlab.points3d([k], [b / scale_b], [S(k, b) / scale_error],
scale_factor=0.1,
color=(1, 1, 1))
mlab.show()
lo = 2.5
L = numpy.zeros((M, M), dtype=float)
for n in range(M):
for m in range(M):
L[n,
m] = 2.5 * ((x_grid[n] - 0.5)**2 + (y_grid[m] - 0.5)**2 <= 1. / 16)
U = 100 * numpy.ones((M, M), dtype=float)
Z, s = projected_gradients(u, O_tilde, dO_tilde, [L, U])
# # Plot with MAYAVI
x = y = list(range(numpy.shape(Z)[0]))
try:
import enthought.mayavi.mlab as mlab
except:
import mayavi.mlab as mlab
mlab.figure()
mlab.view(azimuth=130)
s = mlab.surf(x,
y,
Z,
representation='wireframe',
warp_scale='auto',
line_width=1.)
mlab.savefig('./mayavi_3D_plot.png')
# mlab.show()
#!/usr/bin/env python
import numpy as np
from enthought.mayavi import mlab
x,y=np.ogrid [-2:2:160j,-2:2:160j]
z=abs(x)*np.exp(-x**2-(y/.75)**2)
pl=mlab.surf(x,y,z,warp_scale=2)
mlab.axes(xlabel='x',ylabel='y',zlabel='z')
mlab.outline(pl)
mlab.show()
"""
import numpy as np
from scipy import interpolate
import pylab as py
def func(x, y):
return (x + y) * np.exp(-5.0 * (x**2 + y**2))
x = np.random.uniform(-1.0, 1.0, size=50)
y = np.random.uniform(-1.0, 1.0, size=50)
fvals = func(x, y)
newfunc = interpolate.Rbf(x, y, fvals, function='multiquadric')
xnew, ynew = np.mgrid[-1:1:100j, -1:1:100j]
fnew = newfunc(xnew, ynew)
tru = func(xnew, ynew)
# Create image plot
py.figure(1)
py.clf()
py.imshow(fnew, extent=[-1, 1, -1, 1], cmap=py.cm.jet)
py.scatter(x, y, 30, fvals, cmap=py.cm.jet)
# Create surface plot
from enthought.mayavi import mlab
mlab.clf()
mlab.surf(xnew, ynew, fnew * 2)
from stats.spirrid import orthogonalize
from time import sleep
e_arr = orthogonalize(s.evar_list)
n_e_arr = [e / np.max(np.fabs(e)) for e in e_arr]
max_mu_q = np.max(np.fabs(s.mu_q_arr))
n_mu_q_arr = s.mu_q_arr / max_mu_q
n_std_q_arr = np.sqrt(s.var_q_arr) / max_mu_q
import enthought.mayavi.mlab as m
f = m.figure(1, size=(1000, 500), fgcolor=(0, 0, 0), bgcolor=(1.0, 1.0, 1.0))
s = m.surf(n_e_arr[1], n_e_arr[2], n_mu_q_arr[0, :, :])
m.axes(
s,
color=(0.7, 0.7, 0.7),
extent=(-1, 1, 0, 1, 0, 1),
ranges=(-0.21, 0.21, 0.1, 20, 0, max_mu_q),
xlabel="x",
ylabel="Lr",
zlabel="Force",
)
m.view(-60.0, 70.0, focalpoint=[0.0, 0.45, 0.45])
# Store the information
view = m.view()
roll = m.roll()
#array([[ 0. , 0.5, 1. ]]) #ogrid为什么不是函数 #根据Python的语法，只有在中括号中才能使用用冒号隔开的切片语法，如果ogrid #是函数的话，那么这些切片必须使用slice函数创建，这显然会增加代码的长度。 #利用ogrid的返回值，我能很容易计算x, y网格面上各点的值，或者x, y, z #网格体上各点的值。下面是绘制三维曲面 x * exp(x**2 - y**2) 的程序： import numpy as np from enthought.mayavi import mlab x, y = np.ogrid[-2:2:20j, -2:2:20j] z = x * np.exp( - x**2 - y**2) pl = mlab.surf(x, y, z, warp_scale="auto") mlab.axes(xlabel='x', ylabel='y', zlabel='z') mlab.outline(pl) #2.2.2 ufunc的方法 #ufunc函数本身还有些方法，这些方法只对两个输入一个输出的ufunc函数有效， #其它的ufunc对象调用这些方法时会抛出ValueError异常。 #reduce 方法和Python的reduce函数类似，它沿着axis轴对array进行操作， #相当于将<op>运算符插入到沿axis轴的所有子数组或者元素当中。 #<op>.reduce (array=, axis=0, dtype=None) #例如：
from pylab import *
from enthought.mayavi import mlab
I = 10 * rand(20, 30)
figure(3)
hold(False)
imshow(I)
xlabel(r'first argument of the array, inversed', fontsize=16)
ylabel(r'first argument of the array', fontsize=16)
hold(True)
axis('scaled')
title(r'imshow', fontsize=20)
draw() # or show() on some machines
mlab.figure(1)
mlab.surf(I)
mlab.axes()
import numpy as np
import enthought.mayavi.mlab as mlab
def f(x, y):
return x + y*y
x, y = np.mgrid[1:3:0.1, -2.:2.:0.01]
s = mlab.surf(x, y, f)
x = np.linspace(0, 20, 20000)
#print x[35]
x -= x[70]
#print x
#crack_x = x -
#x = np.linspace( -10 , 10 , 100 )
# eps = sb.get_eps_x_reinf( x )
#
# plt.plot( x, eps, lw = 2, color = 'black' )
# plt.show()
import enthought.mayavi.mlab as m
from stats.spirrid import orthogonalize
resolution = 200
profiles_list = []
forces_arr = np.linspace(0, 6200, resolution)
for i, p in enumerate(forces_arr):
sb.P = p
profiles_list.append(sb.get_eps_x_reinf(x))
profiles_arr = np.array(profiles_list)
param_arr = orthogonalize([x, forces_arr])
norm_param_arr = [ e / np.max(np.fabs(e)) for e in param_arr ]
norm_profiles_arr = profiles_arr / np.max(np.fabs(profiles_arr))
m.surf(norm_param_arr[0], norm_param_arr[1], norm_profiles_arr)
m.show()
power=3,
threshold=10**(-3),
norm=2,
neighbor_type='reduced',
jacobian_file=None,
distance_type='radial')
# Unpack the results and calculate the adjusted data
estimate, residuals, misfits, goals = results
fatiando.mesh.fill(estimate, mesh)
adjusted = gplant.adjustment(data, residuals)
# PLOT THE INVERSION RESULTS
################################################################################
log.info("Plotting")
# Plot the adjusted model plus the skeleton of the synthetic model
fig = mlab.figure()
fig.scene.background = (1, 1, 1)
plot = vis.plot_prism_mesh(seed_mesh, style='surface', label='Density')
plot = vis.plot_prism_mesh(mesh, style='surface', label='Density')
plot = vis.plot_prism_mesh(mesh, style='surface', label='Density')
axes = mlab.axes(plot, nb_labels=5, extent=extent, color=(0, 0, 0))
axes.label_text_property.color = (0, 0, 0)
axes.title_text_property.color = (0, 0, 0)
axes.axes.label_format = "%-#.0f"
mlab.outline(color=(0, 0, 0), extent=extent)
Y, X, Z = utils.extract_matrices(data['gzz'])
mlab.surf(X, Y, Z)
mlab.show()
